mamut-routing-lib 0.1.0

Modern Python library for CVRP and VRPTW benchmark models, validation, BKS management, and snapshot retrieval.

MAMUT-routing-lib

Modern Python library for CVRP and VRPTW benchmark models, validation, BKS management, and snapshot retrieval.

MAMUT project context

This repository is part of the MAMUT project (ANR-22-CE22-0016), an academic research project aiming to advance the state of the art in combinatorial optimization for logistics and transportation problems.

Scope

mamut_routing_lib is a standalone Python contract/runtime layer to work with the routing benchmarks curated in the MAMUT-routing repository. It is inspired by projects like VRPLIB and is intended as a general-purpose library for working with CVRP and VRPTW benchmark instances, both historical and newly generated as well as their associated BKS and metadata.

It provides:

• historical VRPTW benchmark models
• generated CVRP and VRPTW benchmark models
• local benchmark discovery and JSON I/O
• solution checking
• BKS creation and replacement logic
• optional remote snapshot archive retrieval

This repository does not own site generation, publication-history generation, migration pipelines, or solver integrations. It is a pure contract and runtime library for benchmark data management intended to be used by researchers and practitioners alike, both inside and outside the MAMUT project.

Installation

pip install mamut-routing-lib

or, using the modern uv Python package manager:

uv add mamut-routing-lib

Local Loading

from pathlib import Path

from mamut_routing_lib import discover_benchmark_instances

items = discover_benchmark_instances(
    benchmarks_root=Path("/path/to/benchmarks"),
)

Remote Snapshot Retrieval

The optional remote module consumes release manifests and release assets published by a benchmark repository such as MAMUT-routing.

Default environment variables:

• MAMUT_ROUTING_RELEASE_REPO
• MAMUT_ROUTING_GITHUB_TOKEN
• MAMUT_ROUTING_ROOT
• MAMUT_ROUTING_BENCHMARKS_ROOT

Command-line interface

A mamut-routing CLI is available with the optional cli extra:

pip install "mamut-routing-lib[cli]"
# or with uv
uv add "mamut-routing-lib[cli]"

It exposes local benchmark commands by default, plus a remote command group backed by the remote retrieval module:

# List archives available in the latest release of the configured repo
mamut-routing remote --repo ANR-MAMUT/MAMUT-routing list

# Filter by problem-type/benchmark-name
mamut-routing remote list --problem-type CVRP --benchmark-name Mamut2026

# Download (and extract) one or more archives into --benchmarks-dir
mamut-routing --benchmarks-dir ./benchmarks remote \
    fetch CVRP-Mamut2026-snapshot-2026-05-22-28f9199.zip

# Or fetch by filter:
mamut-routing remote fetch --problem-type CVRP --benchmark-name Mamut2026

# Verify local zip checksums against the remote manifest
mamut-routing --benchmarks-dir ./benchmarks remote verify

# Print the parsed manifest as JSON
mamut-routing remote manifest | jq .snapshot_id

Release archives are published at the problem-family level, for example CVRP-Mamut2026 or VRPTW-Sintef2008. Extracted archives are placed in a directory named after the archive stem, containing the archived benchmarks/... tree.

The --benchmarks-dir flag is also read from MAMUT_ROUTING_BENCHMARKS_ROOT or MAMUT_ROUTING_ROOT. Remote flags --repo, --token, and --tag are read from MAMUT_ROUTING_RELEASE_REPO and MAMUT_ROUTING_GITHUB_TOKEN where applicable.

Solving with PyVRP

An optional [pyvrp] extra wraps PyVRP's HGS metaheuristic so users can solve CVRP and VRPTW instances directly from the library.

# Python API only
pip install "mamut-routing-lib[pyvrp]"

# Both the CLI (mamut-routing solve) and the API
pip install "mamut-routing-lib[cli,pyvrp]"

Python:

from mamut_routing_lib import load_benchmark_instance, ObjectiveFunction
from mamut_routing_lib.solvers.pyvrp import solve_instance, solve_and_update_bks

instance = load_benchmark_instance("path/to/instance.vrp.json")
result = solve_instance(instance, time_limit_s=30, seed=42)
print(result.solver_is_feasible, result.solver_cost, result.route_count)

# Or solve-and-write-BKS in one call
result, update = solve_and_update_bks(
    instance,
    instance_path="path/to/instance.vrp.json",
    time_limit_s=30,
    seed=42,
    objective_function=ObjectiveFunction.HIERARCHICAL_VEHICLE_COST,
)
print(update.action if update else "infeasible")

CLI (requires [cli,pyvrp]):

# Inspect what's locally available before solving
mamut-routing --benchmarks-dir ./benchmarks list \
    --problem-type CVRP --benchmark-name Mamut2026

# Include source file paths in the table when needed
mamut-routing --benchmarks-dir ./benchmarks list --show-path

# Pipe the matching paths into solve
mamut-routing --benchmarks-dir ./benchmarks list \
    --problem-type CVRP --paths-only \
    | xargs -r mamut-routing solve --time-limit-s 30

# Solve specific instances
mamut-routing solve path/to/inst1.vrp.json path/to/inst2.vrp.json \
    --time-limit-s 30 --seed 42

# Or discover under --benchmarks-dir and filter
mamut-routing --benchmarks-dir ./benchmarks solve \
    --problem-type VRPTW --benchmark-name Mamut2026 \
    --objective hierarchical_vehicle_cost \
    --time-limit-s 60

Development

# Install editable with CLI extras and test deps
uv pip install -e ".[cli]"
uv pip install pytest

# Hermetic offline test suite (no network)
pytest -v tests/

# Opt-in real-network smoke test (downloads ~1.6 MB from the public MAMUT-routing release)
MAMUT_ROUTING_TEST_NETWORK=1 pytest -v tests/test_remote_network.py